Siitonen, A., Kytövuori, L., Nalls, M.A. et al. Finnish Parkinson’s disease study integrating protein-protein interaction network data with exome sequencing analysis. Sci Rep 9, 18865 (2019). https://doi.org/10.1038/s41598-019-55479-y
Finnish Parkinson’s disease study integrating protein-protein interaction network data with exome sequencing analysis
|Author:||Siitonen, Ari1,2; Kytövuori, Laura1,2; Nalls, Mike A.3,4;|
1Research Unit of Clinical Neuroscience, University of Oulu, Oulu, Finland
2Department of Neurology and Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
3Laboratory for Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
4Data Tecnica International, Glen Echo, MD, 20812, USA
5Institute of Clinical Medicine, Department of Neurology, University of Turku, Turku, Finland
6Division of Clinical Neurosciences, Turku University Hospital, Turku, Finland
7THL, Helsinki, Finland
|Online Access:||PDF Full Text (PDF, 1.2 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020042822723
|Publish Date:|| 2020-04-28
Variants associated with Parkinson’s disease (PD) have generally a small effect size and, therefore, large sample sizes or targeted analyses are required to detect significant associations in a whole exome sequencing (WES) study. Here, we used protein-protein interaction (PPI) information on 36 genes with established or suggested associations with PD to target the analysis of the WES data. We performed an association analysis on WES data from 439 Finnish PD subjects and 855 controls, and included a Finnish population cohort as the replication dataset with 60 PD subjects and 8214 controls. Single variant association (SVA) test in the discovery dataset yielded 11 candidate variants in seven genes, but the associations were not significant in the replication cohort after correction for multiple testing. Polygenic risk score using variants rs2230288 and rs2291312, however, was associated to PD with odds ratio of 2.7 (95% confidence interval 1.4–5.2; p < 2.56e-03). Furthermore, an analysis of the PPI network revealed enriched clusters of biological processes among established and candidate genes, and these functional networks were visualized in the study. We identified novel candidate variants for PD using a gene prioritization based on PPI information, and described why these variants may be involved in the pathogenesis of PD.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
3124 Neurology and psychiatry
We acknowledge our use of the gene set enrichment analysis, GSEA software, and Molecular Signature Database (MSigDB) (Subramanian, Tamayo, et al. (2005), PNAS 102, 15545–15550, http://www.broad.mit.edu/gsea/). The study was supported in part by a grant from the Sigrid Juselius Foundation. For funding details and acknowledgments, please see the Supplementary Material.
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